proteinSummarization {MSstatsTMT} | R Documentation |
We assume missing values are censored and then impute the missing values. Protein-level summarization from PSM level quantification should be performed before testing differentially abundant proteins. After all, normalization between MS runs using normalization channels will be implemented.
proteinSummarization(data, method = "msstats", normalization = TRUE, MBimpute = TRUE, maxQuantileforCensored = NULL)
data |
Name of the output of PDtoMSstatsTMTFormat function or PSM-level quantified data from other tools. It should have columns ProteinName, PeptideSequence, Charge, PSM, Mixture, TechRepMixture, Run, Channel, Condition, BioReplicate, Intensity |
method |
Four different summarization methods to protein-level can be performed : "msstats"(default), "MedianPolish", "Median", "LogSum". |
normalization |
Normalization between MS runs. TRUE(default) needs at least one normalization channel in each MS run, annotated by 'Norm' in Condtion column. It will be performed after protein-level summarization. FALSE will not perform normalization step. If data only has one run, then normalization=FALSE. |
MBimpute |
only for method="msstats". TRUE (default) imputes missing values by Accelated failure model. FALSE uses minimum value to impute the missing value for each PSM. |
maxQuantileforCensored |
We assume missing values are censored. maxQuantileforCensored is Maximum quantile for deciding censored missing value, for instance, 0.999. Default is Null. |
data.frame with protein-level summarization for each run and channel
data(input.pd) quant.pd.msstats <- proteinSummarization(input.pd, method="msstats", normalization=TRUE) head(quant.pd.msstats)